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The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

This study aims to examine the impact of the regulatory focus of the top management team (TMT) members on the technological diversification of firms in high-technology industries based on the upper echelons theory and regulatory focus theory and explore the moderating effect of environmental uncertainty.

This paper uses data on the Chinese Growth Enterprises Market Board (GEM)-listed companies from 2012 to 2016. The authors collected data on TMT regulatory focus from firms’ annual reports by Python programming. A fixed-effects model was used to test our hypotheses.

Results indicate that TMTs with a high promotion focus are associated with greater technological diversification, while TMTs with a high prevention focus are linked to lower technological diversification. Moreover, environmental uncertainty amplifies the positive relationship between promotion-focused TMTs and technological diversification, while it diminishes the negative relationship between prevention-focused TMTs and technological diversification.

This study is limited to high-technology firms listed on the Chinese GEM, which may restrict the generalizability of the findings. Future research could validate these results in different countries and industries to enhance their robustness. Additionally, this study focuses on the impact of TMT regulatory focus on technological diversification; future studies could explore its influence on other strategic decisions, such as digital transformation or innovation strategies.

The results suggest that firms should carefully consider the regulatory focus of their TMT when making strategic decisions regarding technological diversification. Boards of directors should ensure that the TMT’s regulatory focus aligns with the firm’s strategic objectives, particularly in high-technology industries. Moreover, firms should adapt their strategies to the level of environmental uncertainty to better navigate the risks and opportunities presented by a dynamic market environment.

Supportive evidence allows authors to discuss how our findings contribute to the upper echelons theory, as well as the emerging stream of firm technological diversification, which provided valuable psychological insights into the factors influencing TMT strategic decision-making. Meanwhile, this paper integrates the factors of the industry macro-environment to explore the changes in the TMT regulatory focus on firm technological diversification under different contexts.

The literature suggests that increasing the intensity of compensation incentives for corporate venture capital (CVC) managers can contribute to successful exits of direct CVCs. This study explores the impact of compensation incentives on the successful exits of indirect CVCs under different geographical distances between parent companies and indirect CVC managers.

The authors observed the compensation terms of CVC managers through investment announcements made by listed companies and used a probit regression model to test the hypotheses from a sample of 241 investment events with indirect CVCs in China.

The results show that if parent companies are geographically close to the managers of indirect CVCs, increasing the intensity of compensation incentives for managers will help the successful exit of indirect CVCs. However, if parent companies are not geographically close to indirect CVC managers, increasing the intensity of compensation incentives for managers will not promote the successful exit of indirect CVCs.

This study contributes significantly to the CVC literature. First, it sharpens our understanding of the differences in operational mechanisms between direct and indirect CVCs. Second, we find that the threshold returns of indirect CVC managers are non-negligible compensation incentives. Finally, the empirical evidence supports that in indirect CVC investments, the geographical distance between parent companies and managers is concerning because it affects whether compensation incentives contribute to the successful exit of indirect CVCs.